Overview

Dataset statistics

Number of variables42
Number of observations16597
Missing cells60340
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 MiB
Average record size in memory329.0 B

Variable types

Numeric7
Categorical22
Text6
Boolean1
DateTime4
Unsupported2

Alerts

ano_atendimento has constant value ""Constant
fg_ativo has constant value ""Constant
id_orgao has constant value ""Constant
nm_orgao has constant value ""Constant
sg_orgao has constant value ""Constant
nome_familia has constant value ""Constant
status has constant value ""Constant
id_tp_orgao has constant value ""Constant
nu_atendimento is highly overall correlated with id_atendimentoHigh correlation
id_atendimento is highly overall correlated with nu_atendimentoHigh correlation
id_natureza is highly overall correlated with de_naturezaHigh correlation
id_area is highly overall correlated with de_areaHigh correlation
id_municipio is highly overall correlated with id_municipio_fato_ocorrido and 1 other fieldsHigh correlation
id_municipio_fato_ocorrido is highly overall correlated with id_municipioHigh correlation
sg_status_atendimento is highly overall correlated with de_status_atendimentoHigh correlation
de_status_atendimento is highly overall correlated with sg_status_atendimentoHigh correlation
de_natureza is highly overall correlated with id_naturezaHigh correlation
id_programa is highly overall correlated with de_programaHigh correlation
de_programa is highly overall correlated with id_programaHigh correlation
de_area is highly overall correlated with id_area and 2 other fieldsHigh correlation
id_tp_identificacao is highly overall correlated with de_tp_identificacao and 1 other fieldsHigh correlation
de_tp_identificacao is highly overall correlated with id_tp_identificacao and 1 other fieldsHigh correlation
id_forma is highly overall correlated with de_area and 1 other fieldsHigh correlation
de_forma is highly overall correlated with de_area and 1 other fieldsHigh correlation
sg_uf is highly overall correlated with id_municipioHigh correlation
de_sexo_solicitante is highly overall correlated with id_tp_identificacao and 2 other fieldsHigh correlation
de_forma_tratamento is highly overall correlated with de_sexo_solicitanteHigh correlation
sg_status_atendimento is highly imbalanced (81.3%)Imbalance
de_status_atendimento is highly imbalanced (81.3%)Imbalance
id_programa is highly imbalanced (63.7%)Imbalance
de_programa is highly imbalanced (63.7%)Imbalance
id_forma is highly imbalanced (82.9%)Imbalance
de_forma is highly imbalanced (82.9%)Imbalance
id_priorizacao is highly imbalanced (99.9%)Imbalance
de_priorizacao is highly imbalanced (99.9%)Imbalance
sg_uf is highly imbalanced (86.3%)Imbalance
id_assunto has 645 (3.9%) missing valuesMissing
de_assunto has 645 (3.9%) missing valuesMissing
id_programa has 645 (3.9%) missing valuesMissing
de_programa has 645 (3.9%) missing valuesMissing
id_area has 645 (3.9%) missing valuesMissing
de_area has 645 (3.9%) missing valuesMissing
id_priorizacao has 645 (3.9%) missing valuesMissing
de_priorizacao has 645 (3.9%) missing valuesMissing
de_bairro has 6964 (42.0%) missing valuesMissing
de_cep has 6653 (40.1%) missing valuesMissing
de_outro_local has 16597 (100.0%) missing valuesMissing
de_sexo_solicitante has 382 (2.3%) missing valuesMissing
nu_idade_solicitante has 16597 (100.0%) missing valuesMissing
de_forma_tratamento has 7896 (47.6%) missing valuesMissing
nu_atendimento has unique valuesUnique
id_atendimento has unique valuesUnique
ch_atendimento has unique valuesUnique
de_outro_local is an unsupported type, check if it needs cleaning or further analysisUnsupported
nu_idade_solicitante is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-06 12:36:56.302239
Analysis finished2023-07-06 12:37:16.149457
Duration19.85 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

nu_atendimento
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct16597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9454.6423
Minimum1
Maximum18887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:16.275892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile979.6
Q14718
median9406
Q314236
95-th percentile17964.2
Maximum18887
Range18886
Interquartile range (IQR)9518

Descriptive statistics

Standard deviation5459.1817
Coefficient of variation (CV)0.57740753
Kurtosis-1.2076136
Mean9454.6423
Median Absolute Deviation (MAD)4756
Skewness0.0122141
Sum1.569187 × 108
Variance29802664
MonotonicityStrictly increasing
2023-07-06T12:37:16.547274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
12650 1
 
< 0.1%
12617 1
 
< 0.1%
12618 1
 
< 0.1%
12619 1
 
< 0.1%
12620 1
 
< 0.1%
12621 1
 
< 0.1%
12622 1
 
< 0.1%
12624 1
 
< 0.1%
12625 1
 
< 0.1%
Other values (16587) 16587
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
18887 1
< 0.1%
18886 1
< 0.1%
18885 1
< 0.1%
18884 1
< 0.1%
18883 1
< 0.1%
18882 1
< 0.1%
18881 1
< 0.1%
18880 1
< 0.1%
18879 1
< 0.1%
18878 1
< 0.1%

ano_atendimento
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2023
16597 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters66388
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 16597
100.0%

Length

2023-07-06T12:37:16.819242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:17.051146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2023 16597
100.0%

Most occurring characters

ValueCountFrequency (%)
2 33194
50.0%
0 16597
25.0%
3 16597
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66388
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 33194
50.0%
0 16597
25.0%
3 16597
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 33194
50.0%
0 16597
25.0%
3 16597
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 33194
50.0%
0 16597
25.0%
3 16597
25.0%

id_atendimento
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct16597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean476067.84
Minimum466499
Maximum485596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:17.335427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum466499
5-th percentile467485.6
Q1471276
median476028
Q3480907
95-th percentile484663.2
Maximum485596
Range19097
Interquartile range (IQR)9631

Descriptive statistics

Standard deviation5520.5909
Coefficient of variation (CV)0.011596227
Kurtosis-1.2070277
Mean476067.84
Median Absolute Deviation (MAD)4811
Skewness0.0091070718
Sum7.901298 × 109
Variance30476924
MonotonicityStrictly increasing
2023-07-06T12:37:17.703353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
466499 1
 
< 0.1%
479300 1
 
< 0.1%
479267 1
 
< 0.1%
479268 1
 
< 0.1%
479269 1
 
< 0.1%
479270 1
 
< 0.1%
479271 1
 
< 0.1%
479272 1
 
< 0.1%
479274 1
 
< 0.1%
479275 1
 
< 0.1%
Other values (16587) 16587
99.9%
ValueCountFrequency (%)
466499 1
< 0.1%
466500 1
< 0.1%
466502 1
< 0.1%
466503 1
< 0.1%
466504 1
< 0.1%
466505 1
< 0.1%
466506 1
< 0.1%
466507 1
< 0.1%
466508 1
< 0.1%
466509 1
< 0.1%
ValueCountFrequency (%)
485596 1
< 0.1%
485595 1
< 0.1%
485594 1
< 0.1%
485593 1
< 0.1%
485592 1
< 0.1%
485591 1
< 0.1%
485590 1
< 0.1%
485589 1
< 0.1%
485588 1
< 0.1%
485587 1
< 0.1%

ch_atendimento
Text

UNIQUE 

Distinct16597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:18.165344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length10
Mean length10.393686
Min length10

Characters and Unicode

Total characters172504
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16597 ?
Unique (%)100.0%

Sample

1st row2023000001
2nd row2023000002
3rd row2023000004
4th row2023000005
5th row2023000006
ValueCountFrequency (%)
2023000001 1
 
< 0.1%
2023000072 1
 
< 0.1%
2023000018 1
 
< 0.1%
2023000004 1
 
< 0.1%
2023000005 1
 
< 0.1%
2023000006 1
 
< 0.1%
2023000007 1
 
< 0.1%
2023000008 1
 
< 0.1%
2023000009 1
 
< 0.1%
2023000010 1
 
< 0.1%
Other values (16587) 16587
99.9%
2023-07-06T12:37:18.959644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 50511
29.3%
2 40200
23.3%
3 23619
13.7%
1 14487
 
8.4%
6 7035
 
4.1%
5 6973
 
4.0%
4 6970
 
4.0%
7 6942
 
4.0%
8 6187
 
3.6%
9 6016
 
3.5%
Other values (6) 3564
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168940
97.9%
Uppercase Letter 2970
 
1.7%
Dash Punctuation 594
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50511
29.9%
2 40200
23.8%
3 23619
14.0%
1 14487
 
8.6%
6 7035
 
4.2%
5 6973
 
4.1%
4 6970
 
4.1%
7 6942
 
4.1%
8 6187
 
3.7%
9 6016
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
E 594
20.0%
S 594
20.0%
I 594
20.0%
C 594
20.0%
T 594
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169534
98.3%
Latin 2970
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50511
29.8%
2 40200
23.7%
3 23619
13.9%
1 14487
 
8.5%
6 7035
 
4.1%
5 6973
 
4.1%
4 6970
 
4.1%
7 6942
 
4.1%
8 6187
 
3.6%
9 6016
 
3.5%
Latin
ValueCountFrequency (%)
E 594
20.0%
S 594
20.0%
I 594
20.0%
C 594
20.0%
T 594
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50511
29.3%
2 40200
23.3%
3 23619
13.7%
1 14487
 
8.4%
6 7035
 
4.1%
5 6973
 
4.0%
4 6970
 
4.0%
7 6942
 
4.0%
8 6187
 
3.6%
9 6016
 
3.5%
Other values (6) 3564
 
2.1%

sg_status_atendimento
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
C
15798 
E
 
743
P
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

Length

2023-07-06T12:37:19.150059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:19.322523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 15798
95.2%
e 743
 
4.5%
p 56
 
0.3%

Most occurring characters

ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16597
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 16597
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

de_status_atendimento
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Concluido
15798 
Encaminhado
 
743
Pendente
 
56

Length

Max length11
Median length9
Mean length9.0861601
Min length8

Characters and Unicode

Total characters150803
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConcluido
2nd rowConcluido
3rd rowConcluido
4th rowConcluido
5th rowConcluido

Common Values

ValueCountFrequency (%)
Concluido 15798
95.2%
Encaminhado 743
 
4.5%
Pendente 56
 
0.3%

Length

2023-07-06T12:37:19.466345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:19.645428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
concluido 15798
95.2%
encaminhado 743
 
4.5%
pendente 56
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 32339
21.4%
n 17396
11.5%
d 16597
11.0%
c 16541
11.0%
i 16541
11.0%
C 15798
10.5%
l 15798
10.5%
u 15798
10.5%
a 1486
 
1.0%
E 743
 
0.5%
Other values (5) 1766
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134206
89.0%
Uppercase Letter 16597
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 32339
24.1%
n 17396
13.0%
d 16597
12.4%
c 16541
12.3%
i 16541
12.3%
l 15798
11.8%
u 15798
11.8%
a 1486
 
1.1%
m 743
 
0.6%
h 743
 
0.6%
Other values (2) 224
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 15798
95.2%
E 743
 
4.5%
P 56
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 150803
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 32339
21.4%
n 17396
11.5%
d 16597
11.0%
c 16541
11.0%
i 16541
11.0%
C 15798
10.5%
l 15798
10.5%
u 15798
10.5%
a 1486
 
1.0%
E 743
 
0.5%
Other values (5) 1766
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 32339
21.4%
n 17396
11.5%
d 16597
11.0%
c 16541
11.0%
i 16541
11.0%
C 15798
10.5%
l 15798
10.5%
u 15798
10.5%
a 1486
 
1.0%
E 743
 
0.5%
Other values (5) 1766
 
1.2%

fg_ativo
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.3 KiB
True
16597 
ValueCountFrequency (%)
True 16597
100.0%
2023-07-06T12:37:19.801309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct16583
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Minimum2023-01-01 03:29:01.614890
Maximum2023-07-05 23:29:08.467600
2023-07-06T12:37:19.946497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:20.126772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct16504
Distinct (%)99.8%
Missing53
Missing (%)0.3%
Memory size129.8 KiB
Minimum2023-01-02 09:09:38
Maximum2023-07-05 17:36:56
2023-07-06T12:37:20.336344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:20.524802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct16583
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Minimum2023-01-01 03:29:01.614890
Maximum2023-07-05 23:29:08.467600
2023-07-06T12:37:20.720083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:20.906088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct186
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Minimum2023-01-31 00:00:00
Maximum2023-08-04 00:00:00
2023-07-06T12:37:21.378762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:21.568815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

id_orgao
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2
16597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 16597
100.0%

Length

2023-07-06T12:37:21.756143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:21.912692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 16597
100.0%

Most occurring characters

ValueCountFrequency (%)
2 16597
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16597
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16597
100.0%

nm_orgao
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Ouvidoria Geral do Estado
16597 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters414925
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOuvidoria Geral do Estado
2nd rowOuvidoria Geral do Estado
3rd rowOuvidoria Geral do Estado
4th rowOuvidoria Geral do Estado
5th rowOuvidoria Geral do Estado

Common Values

ValueCountFrequency (%)
Ouvidoria Geral do Estado 16597
100.0%

Length

2023-07-06T12:37:22.037285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:22.191409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ouvidoria 16597
25.0%
geral 16597
25.0%
do 16597
25.0%
estado 16597
25.0%

Most occurring characters

ValueCountFrequency (%)
d 49791
12.0%
o 49791
12.0%
a 49791
12.0%
49791
12.0%
i 33194
 
8.0%
r 33194
 
8.0%
O 16597
 
4.0%
u 16597
 
4.0%
v 16597
 
4.0%
G 16597
 
4.0%
Other values (5) 82985
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 315343
76.0%
Space Separator 49791
 
12.0%
Uppercase Letter 49791
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 49791
15.8%
o 49791
15.8%
a 49791
15.8%
i 33194
10.5%
r 33194
10.5%
u 16597
 
5.3%
v 16597
 
5.3%
e 16597
 
5.3%
l 16597
 
5.3%
s 16597
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 16597
33.3%
G 16597
33.3%
E 16597
33.3%
Space Separator
ValueCountFrequency (%)
49791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 365134
88.0%
Common 49791
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 49791
13.6%
o 49791
13.6%
a 49791
13.6%
i 33194
9.1%
r 33194
9.1%
O 16597
 
4.5%
u 16597
 
4.5%
v 16597
 
4.5%
G 16597
 
4.5%
e 16597
 
4.5%
Other values (4) 66388
18.2%
Common
ValueCountFrequency (%)
49791
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 49791
12.0%
o 49791
12.0%
a 49791
12.0%
49791
12.0%
i 33194
 
8.0%
r 33194
 
8.0%
O 16597
 
4.0%
u 16597
 
4.0%
v 16597
 
4.0%
G 16597
 
4.0%
Other values (5) 82985
20.0%

sg_orgao
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
OGE
16597 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters49791
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOGE
2nd rowOGE
3rd rowOGE
4th rowOGE
5th rowOGE

Common Values

ValueCountFrequency (%)
OGE 16597
100.0%

Length

2023-07-06T12:37:22.323072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:22.475806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
oge 16597
100.0%

Most occurring characters

ValueCountFrequency (%)
O 16597
33.3%
G 16597
33.3%
E 16597
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49791
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 16597
33.3%
G 16597
33.3%
E 16597
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 49791
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 16597
33.3%
G 16597
33.3%
E 16597
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 16597
33.3%
G 16597
33.3%
E 16597
33.3%

nome_familia
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
SC::Ouvidoria Geral do Estado - OGE
16597 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters580895
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC::Ouvidoria Geral do Estado - OGE
2nd rowSC::Ouvidoria Geral do Estado - OGE
3rd rowSC::Ouvidoria Geral do Estado - OGE
4th rowSC::Ouvidoria Geral do Estado - OGE
5th rowSC::Ouvidoria Geral do Estado - OGE

Common Values

ValueCountFrequency (%)
SC::Ouvidoria Geral do Estado - OGE 16597
100.0%

Length

2023-07-06T12:37:22.608946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:22.765782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sc::ouvidoria 16597
16.7%
geral 16597
16.7%
do 16597
16.7%
estado 16597
16.7%
16597
16.7%
oge 16597
16.7%

Most occurring characters

ValueCountFrequency (%)
82985
14.3%
d 49791
 
8.6%
o 49791
 
8.6%
a 49791
 
8.6%
r 33194
 
5.7%
: 33194
 
5.7%
O 33194
 
5.7%
i 33194
 
5.7%
E 33194
 
5.7%
G 33194
 
5.7%
Other values (9) 149373
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 315343
54.3%
Uppercase Letter 132776
22.9%
Space Separator 82985
 
14.3%
Other Punctuation 33194
 
5.7%
Dash Punctuation 16597
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 49791
15.8%
o 49791
15.8%
a 49791
15.8%
r 33194
10.5%
i 33194
10.5%
l 16597
 
5.3%
t 16597
 
5.3%
s 16597
 
5.3%
e 16597
 
5.3%
v 16597
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 33194
25.0%
E 33194
25.0%
G 33194
25.0%
S 16597
12.5%
C 16597
12.5%
Space Separator
ValueCountFrequency (%)
82985
100.0%
Other Punctuation
ValueCountFrequency (%)
: 33194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 448119
77.1%
Common 132776
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 49791
11.1%
o 49791
11.1%
a 49791
11.1%
r 33194
 
7.4%
O 33194
 
7.4%
i 33194
 
7.4%
E 33194
 
7.4%
G 33194
 
7.4%
l 16597
 
3.7%
t 16597
 
3.7%
Other values (6) 99582
22.2%
Common
ValueCountFrequency (%)
82985
62.5%
: 33194
 
25.0%
- 16597
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 580895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82985
14.3%
d 49791
 
8.6%
o 49791
 
8.6%
a 49791
 
8.6%
r 33194
 
5.7%
: 33194
 
5.7%
O 33194
 
5.7%
i 33194
 
5.7%
E 33194
 
5.7%
G 33194
 
5.7%
Other values (9) 149373
25.7%

status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
A
16597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 16597
100.0%

Length

2023-07-06T12:37:22.904084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:23.060264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 16597
100.0%

Most occurring characters

ValueCountFrequency (%)
A 16597
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16597
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 16597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16597
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 16597
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 16597
100.0%

id_tp_orgao
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
9
16597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 16597
100.0%

Length

2023-07-06T12:37:23.201039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:23.355191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9 16597
100.0%

Most occurring characters

ValueCountFrequency (%)
9 16597
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 16597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 16597
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 16597
100.0%

id_natureza
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5400976
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:23.466460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7474039
Coefficient of variation (CV)0.68792786
Kurtosis34.905669
Mean2.5400976
Median Absolute Deviation (MAD)1
Skewness5.1372086
Sum42158
Variance3.0534204
MonotonicityNot monotonic
2023-07-06T12:37:23.593057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 7577
45.7%
2 5350
32.2%
1 3063
18.5%
8 279
 
1.7%
16 160
 
1.0%
4 148
 
0.9%
14 20
 
0.1%
ValueCountFrequency (%)
1 3063
18.5%
2 5350
32.2%
3 7577
45.7%
4 148
 
0.9%
8 279
 
1.7%
14 20
 
0.1%
16 160
 
1.0%
ValueCountFrequency (%)
16 160
 
1.0%
14 20
 
0.1%
8 279
 
1.7%
4 148
 
0.9%
3 7577
45.7%
2 5350
32.2%
1 3063
18.5%

de_natureza
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Solicitação
7577 
Reclamação
5350 
Denúncia
3063 
Elogio
 
279
Denúncia (Disque 100)
 
160
Other values (2)
 
168

Length

Max length61
Median length21
Mean length10.16985
Min length6

Characters and Unicode

Total characters168789
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolicitação
2nd rowSolicitação
3rd rowReclamação
4th rowSolicitação
5th rowReclamação

Common Values

ValueCountFrequency (%)
Solicitação 7577
45.7%
Reclamação 5350
32.2%
Denúncia 3063
18.5%
Elogio 279
 
1.7%
Denúncia (Disque 100) 160
 
1.0%
Sugestão 148
 
0.9%
Solicitação Documentos/Informações/Lei de Acesso à Informação 20
 
0.1%

Length

2023-07-06T12:37:23.762902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:23.968436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
solicitação 7597
44.6%
reclamação 5350
31.4%
denúncia 3223
18.9%
elogio 279
 
1.6%
disque 160
 
0.9%
100 160
 
0.9%
sugestão 148
 
0.9%
documentos/informações/lei 20
 
0.1%
de 20
 
0.1%
acesso 20
 
0.1%
Other values (2) 40
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 21560
12.8%
o 21370
12.7%
i 18876
11.2%
c 16210
9.6%
l 13226
7.8%
ã 13115
7.8%
ç 12987
7.7%
e 8981
 
5.3%
t 7765
 
4.6%
S 7745
 
4.6%
Other values (24) 26954
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 150672
89.3%
Uppercase Letter 16857
 
10.0%
Decimal Number 480
 
0.3%
Space Separator 420
 
0.2%
Close Punctuation 160
 
0.1%
Open Punctuation 160
 
0.1%
Other Punctuation 40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21560
14.3%
o 21370
14.2%
i 18876
12.5%
c 16210
10.8%
l 13226
8.8%
ã 13115
8.7%
ç 12987
8.6%
e 8981
6.0%
t 7765
 
5.2%
n 6506
 
4.3%
Other values (11) 10076
6.7%
Uppercase Letter
ValueCountFrequency (%)
S 7745
45.9%
R 5350
31.7%
D 3403
20.2%
E 279
 
1.7%
I 40
 
0.2%
L 20
 
0.1%
A 20
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 320
66.7%
1 160
33.3%
Space Separator
ValueCountFrequency (%)
420
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167529
99.3%
Common 1260
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21560
12.9%
o 21370
12.8%
i 18876
11.3%
c 16210
9.7%
l 13226
7.9%
ã 13115
7.8%
ç 12987
7.8%
e 8981
 
5.4%
t 7765
 
4.6%
S 7745
 
4.6%
Other values (18) 25694
15.3%
Common
ValueCountFrequency (%)
420
33.3%
0 320
25.4%
1 160
 
12.7%
) 160
 
12.7%
( 160
 
12.7%
/ 40
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139424
82.6%
None 29365
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 21560
15.5%
o 21370
15.3%
i 18876
13.5%
c 16210
11.6%
l 13226
9.5%
e 8981
6.4%
t 7765
 
5.6%
S 7745
 
5.6%
n 6506
 
4.7%
m 5410
 
3.9%
Other values (19) 11775
8.4%
None
ValueCountFrequency (%)
ã 13115
44.7%
ç 12987
44.2%
ú 3223
 
11.0%
õ 20
 
0.1%
à 20
 
0.1%

id_assunto
Real number (ℝ)

MISSING 

Distinct390
Distinct (%)2.4%
Missing645
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean968.3577
Minimum2
Maximum1754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:24.170315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile115
Q1261
median1316
Q31497
95-th percentile1618
Maximum1754
Range1752
Interquartile range (IQR)1236

Descriptive statistics

Standard deviation617.39909
Coefficient of variation (CV)0.63757338
Kurtosis-1.7158954
Mean968.3577
Median Absolute Deviation (MAD)295
Skewness-0.30739412
Sum15447242
Variance381181.63
MonotonicityNot monotonic
2023-07-06T12:37:24.357729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1611 2210
 
13.3%
1449 1096
 
6.6%
117 882
 
5.3%
115 820
 
4.9%
242 648
 
3.9%
1444 487
 
2.9%
1354 372
 
2.2%
1308 365
 
2.2%
503 343
 
2.1%
1497 294
 
1.8%
Other values (380) 8435
50.8%
(Missing) 645
 
3.9%
ValueCountFrequency (%)
2 64
0.4%
18 107
0.6%
19 36
 
0.2%
21 1
 
< 0.1%
28 4
 
< 0.1%
29 14
 
0.1%
40 2
 
< 0.1%
46 4
 
< 0.1%
61 5
 
< 0.1%
62 3
 
< 0.1%
ValueCountFrequency (%)
1754 2
 
< 0.1%
1753 14
0.1%
1752 1
 
< 0.1%
1751 1
 
< 0.1%
1750 1
 
< 0.1%
1749 1
 
< 0.1%
1746 3
 
< 0.1%
1745 1
 
< 0.1%
1742 1
 
< 0.1%
1741 1
 
< 0.1%

de_assunto
Text

MISSING 

Distinct390
Distinct (%)2.4%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
2023-07-06T12:37:24.686000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length60
Median length50
Mean length25.377069
Min length3

Characters and Unicode

Total characters404815
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.4%

Sample

1st rowNão Foi Possível Compreender
2nd rowNão Foi Possível Compreender
3rd rowManifestação Incompleta (Falta Dados)
4th rowFiscalização ambiental
5th rowManifestação Incompleta (Falta Dados)
ValueCountFrequency (%)
de 3935
 
7.8%
falta 2600
 
5.2%
dados 2356
 
4.7%
manifestação 2226
 
4.4%
incompleta 2210
 
4.4%
não 1342
 
2.7%
do 1212
 
2.4%
atendimento 1208
 
2.4%
compreender 1096
 
2.2%
possível 1096
 
2.2%
Other values (587) 31014
61.7%
2023-07-06T12:37:25.258075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 39505
 
9.8%
o 37757
 
9.3%
e 35183
 
8.7%
34353
 
8.5%
i 26013
 
6.4%
s 22840
 
5.6%
t 22735
 
5.6%
n 19710
 
4.9%
r 18410
 
4.5%
d 16795
 
4.1%
Other values (62) 131514
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 328266
81.1%
Uppercase Letter 35159
 
8.7%
Space Separator 34353
 
8.5%
Close Punctuation 2429
 
0.6%
Open Punctuation 2429
 
0.6%
Other Punctuation 1184
 
0.3%
Decimal Number 721
 
0.2%
Dash Punctuation 274
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39505
12.0%
o 37757
11.5%
e 35183
10.7%
i 26013
 
7.9%
s 22840
 
7.0%
t 22735
 
6.9%
n 19710
 
6.0%
r 18410
 
5.6%
d 16795
 
5.1%
l 15492
 
4.7%
Other values (25) 73826
22.5%
Uppercase Letter
ValueCountFrequency (%)
F 4790
13.6%
C 4761
13.5%
D 4345
12.4%
I 3420
9.7%
M 3143
8.9%
A 3018
8.6%
P 2899
8.2%
N 1630
 
4.6%
S 1556
 
4.4%
E 1210
 
3.4%
Other values (12) 4387
12.5%
Decimal Number
ValueCountFrequency (%)
2 150
20.8%
1 144
20.0%
0 127
17.6%
3 111
15.4%
8 98
13.6%
9 73
10.1%
7 18
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 616
52.0%
, 493
41.6%
. 73
 
6.2%
' 2
 
0.2%
Space Separator
ValueCountFrequency (%)
34353
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2429
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2429
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363425
89.8%
Common 41390
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39505
 
10.9%
o 37757
 
10.4%
e 35183
 
9.7%
i 26013
 
7.2%
s 22840
 
6.3%
t 22735
 
6.3%
n 19710
 
5.4%
r 18410
 
5.1%
d 16795
 
4.6%
l 15492
 
4.3%
Other values (47) 108985
30.0%
Common
ValueCountFrequency (%)
34353
83.0%
) 2429
 
5.9%
( 2429
 
5.9%
/ 616
 
1.5%
, 493
 
1.2%
- 274
 
0.7%
2 150
 
0.4%
1 144
 
0.3%
0 127
 
0.3%
3 111
 
0.3%
Other values (5) 264
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385667
95.3%
None 19148
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 39505
 
10.2%
o 37757
 
9.8%
e 35183
 
9.1%
34353
 
8.9%
i 26013
 
6.7%
s 22840
 
5.9%
t 22735
 
5.9%
n 19710
 
5.1%
r 18410
 
4.8%
d 16795
 
4.4%
Other values (49) 112366
29.1%
None
ValueCountFrequency (%)
ã 6377
33.3%
ç 6284
32.8%
ê 1789
 
9.3%
í 1504
 
7.9%
á 1097
 
5.7%
é 862
 
4.5%
õ 384
 
2.0%
ú 274
 
1.4%
à 251
 
1.3%
â 185
 
1.0%
Other values (3) 141
 
0.7%

id_programa
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
4.0
13783 
5.0
2167 
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47856
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row5.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 13783
83.0%
5.0 2167
 
13.1%
2.0 2
 
< 0.1%
(Missing) 645
 
3.9%

Length

2023-07-06T12:37:25.443741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:25.607927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 13783
86.4%
5.0 2167
 
13.6%
2.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
4 13783
28.8%
5 2167
 
4.5%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31904
66.7%
Other Punctuation 15952
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15952
50.0%
4 13783
43.2%
5 2167
 
6.8%
2 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
4 13783
28.8%
5 2167
 
4.5%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
4 13783
28.8%
5 2167
 
4.5%
2 2
 
< 0.1%

de_programa
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
Ouvidoria externa
13783 
Ouvidoria interna
2167 
Ouvidoria ambiental
 
2

Length

Max length19
Median length17
Mean length17.000251
Min length17

Characters and Unicode

Total characters271188
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOuvidoria externa
2nd rowOuvidoria externa
3rd rowOuvidoria interna
4th rowOuvidoria externa
5th rowOuvidoria externa

Common Values

ValueCountFrequency (%)
Ouvidoria externa 13783
83.0%
Ouvidoria interna 2167
 
13.1%
Ouvidoria ambiental 2
 
< 0.1%
(Missing) 645
 
3.9%

Length

2023-07-06T12:37:25.761862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:25.951826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ouvidoria 15952
50.0%
externa 13783
43.2%
interna 2167
 
6.8%
ambiental 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 34073
12.6%
a 31906
11.8%
r 31902
11.8%
e 29735
11.0%
n 18119
 
6.7%
O 15952
 
5.9%
u 15952
 
5.9%
v 15952
 
5.9%
d 15952
 
5.9%
o 15952
 
5.9%
Other values (6) 45693
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239284
88.2%
Uppercase Letter 15952
 
5.9%
Space Separator 15952
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 34073
14.2%
a 31906
13.3%
r 31902
13.3%
e 29735
12.4%
n 18119
7.6%
u 15952
6.7%
v 15952
6.7%
d 15952
6.7%
o 15952
6.7%
t 15952
6.7%
Other values (4) 13789
5.8%
Uppercase Letter
ValueCountFrequency (%)
O 15952
100.0%
Space Separator
ValueCountFrequency (%)
15952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 255236
94.1%
Common 15952
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 34073
13.3%
a 31906
12.5%
r 31902
12.5%
e 29735
11.7%
n 18119
7.1%
O 15952
6.2%
u 15952
6.2%
v 15952
6.2%
d 15952
6.2%
o 15952
6.2%
Other values (5) 29741
11.7%
Common
ValueCountFrequency (%)
15952
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 34073
12.6%
a 31906
11.8%
r 31902
11.8%
e 29735
11.0%
n 18119
 
6.7%
O 15952
 
5.9%
u 15952
 
5.9%
v 15952
 
5.9%
d 15952
 
5.9%
o 15952
 
5.9%
Other values (6) 45693
16.8%

id_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)0.3%
Missing645
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean103.15628
Minimum1
Maximum218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:26.111413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median126
Q3170
95-th percentile205
Maximum218
Range217
Interquartile range (IQR)166

Descriptive statistics

Standard deviation81.669501
Coefficient of variation (CV)0.79170653
Kurtosis-1.6957471
Mean103.15628
Median Absolute Deviation (MAD)79
Skewness-0.17120849
Sum1645549
Variance6669.9074
MonotonicityNot monotonic
2023-07-06T12:37:26.288867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4 4081
24.6%
155 3347
20.2%
126 1534
 
9.2%
170 1390
 
8.4%
205 1111
 
6.7%
203 1089
 
6.6%
1 404
 
2.4%
8 375
 
2.3%
213 344
 
2.1%
27 318
 
1.9%
Other values (30) 1959
11.8%
(Missing) 645
 
3.9%
ValueCountFrequency (%)
1 404
 
2.4%
2 173
 
1.0%
4 4081
24.6%
5 15
 
0.1%
8 375
 
2.3%
13 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
18 304
 
1.8%
21 7
 
< 0.1%
ValueCountFrequency (%)
218 2
 
< 0.1%
213 344
 
2.1%
212 137
 
0.8%
209 89
 
0.5%
205 1111
6.7%
203 1089
6.6%
195 271
 
1.6%
194 21
 
0.1%
193 2
 
< 0.1%
170 1390
8.4%

de_area
Categorical

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)0.3%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
Educação
4081 
Segurança Pública
3347 
DETRAN
1534 
FATMA
1390 
Não Aplicável
1111 
Other values (35)
4489 

Length

Max length40
Median length37
Mean length11.761535
Min length5

Characters and Unicode

Total characters187620
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowNão Identificável
2nd rowNão Identificável
3rd rowNão Identificável
4th rowFATMA
5th rowSegurança Pública

Common Values

ValueCountFrequency (%)
Educação 4081
24.6%
Segurança Pública 3347
20.2%
DETRAN 1534
 
9.2%
FATMA 1390
 
8.4%
Não Aplicável 1111
 
6.7%
Não Identificável 1089
 
6.6%
Saúde 404
 
2.4%
Agricultura 375
 
2.3%
Disque 100 344
 
2.1%
Infraestrutura 318
 
1.9%
Other values (30) 1959
11.8%
(Missing) 645
 
3.9%

Length

2023-07-06T12:37:26.474878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
educação 4218
18.0%
pública 3347
14.3%
segurança 3347
14.3%
não 2200
9.4%
detran 1534
 
6.5%
fatma 1390
 
5.9%
aplicável 1111
 
4.7%
identificável 1089
 
4.6%
administração 477
 
2.0%
saúde 404
 
1.7%
Other values (49) 4346
18.5%

Most occurring characters

ValueCountFrequency (%)
a 18797
 
10.0%
c 10887
 
5.8%
i 10434
 
5.6%
e 10207
 
5.4%
u 9876
 
5.3%
o 9382
 
5.0%
l 8325
 
4.4%
ç 8093
 
4.3%
n 7580
 
4.0%
7511
 
4.0%
Other values (42) 86528
46.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141684
75.5%
Uppercase Letter 37107
 
19.8%
Space Separator 7511
 
4.0%
Decimal Number 1032
 
0.6%
Other Punctuation 285
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18797
13.3%
c 10887
 
7.7%
i 10434
 
7.4%
e 10207
 
7.2%
u 9876
 
7.0%
o 9382
 
6.6%
l 8325
 
5.9%
ç 8093
 
5.7%
n 7580
 
5.3%
ã 6945
 
4.9%
Other values (18) 41158
29.0%
Uppercase Letter
ValueCountFrequency (%)
A 6830
18.4%
E 6182
16.7%
S 4330
11.7%
N 3981
10.7%
P 3791
10.2%
T 2967
8.0%
D 2074
 
5.6%
F 1596
 
4.3%
R 1553
 
4.2%
I 1410
 
3.8%
Other values (8) 2393
 
6.4%
Decimal Number
ValueCountFrequency (%)
0 688
66.7%
1 344
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 246
86.3%
, 39
 
13.7%
Space Separator
ValueCountFrequency (%)
7511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178791
95.3%
Common 8829
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18797
 
10.5%
c 10887
 
6.1%
i 10434
 
5.8%
e 10207
 
5.7%
u 9876
 
5.5%
o 9382
 
5.2%
l 8325
 
4.7%
ç 8093
 
4.5%
n 7580
 
4.2%
ã 6945
 
3.9%
Other values (36) 78265
43.8%
Common
ValueCountFrequency (%)
7511
85.1%
0 688
 
7.8%
1 344
 
3.9%
/ 246
 
2.8%
, 39
 
0.4%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165900
88.4%
None 21720
 
11.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18797
 
11.3%
c 10887
 
6.6%
i 10434
 
6.3%
e 10207
 
6.2%
u 9876
 
6.0%
o 9382
 
5.7%
l 8325
 
5.0%
n 7580
 
4.6%
7511
 
4.5%
r 6871
 
4.1%
Other values (35) 66030
39.8%
None
ValueCountFrequency (%)
ç 8093
37.3%
ã 6945
32.0%
ú 3751
17.3%
á 2465
 
11.3%
Á 249
 
1.1%
ô 176
 
0.8%
ê 41
 
0.2%

id_tp_identificacao
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2
7727 
1
7479 
3
1391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

Length

2023-07-06T12:37:26.645704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:26.811887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

Most occurring characters

ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 16597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7727
46.6%
1 7479
45.1%
3 1391
 
8.4%

de_tp_identificacao
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Identificado
7727 
Anônimo
7479 
Sigiloso
1391 

Length

Max length12
Median length8
Mean length9.4116407
Min length7

Characters and Unicode

Total characters156205
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnônimo
2nd rowAnônimo
3rd rowIdentificado
4th rowAnônimo
5th rowIdentificado

Common Values

ValueCountFrequency (%)
Identificado 7727
46.6%
Anônimo 7479
45.1%
Sigiloso 1391
 
8.4%

Length

2023-07-06T12:37:26.967021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:27.142693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
identificado 7727
46.6%
anônimo 7479
45.1%
sigiloso 1391
 
8.4%

Most occurring characters

ValueCountFrequency (%)
i 25715
16.5%
n 22685
14.5%
o 17988
11.5%
d 15454
9.9%
I 7727
 
4.9%
c 7727
 
4.9%
a 7727
 
4.9%
f 7727
 
4.9%
t 7727
 
4.9%
e 7727
 
4.9%
Other values (7) 28001
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139608
89.4%
Uppercase Letter 16597
 
10.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 25715
18.4%
n 22685
16.2%
o 17988
12.9%
d 15454
11.1%
c 7727
 
5.5%
a 7727
 
5.5%
f 7727
 
5.5%
t 7727
 
5.5%
e 7727
 
5.5%
ô 7479
 
5.4%
Other values (4) 11652
8.3%
Uppercase Letter
ValueCountFrequency (%)
I 7727
46.6%
A 7479
45.1%
S 1391
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 156205
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 25715
16.5%
n 22685
14.5%
o 17988
11.5%
d 15454
9.9%
I 7727
 
4.9%
c 7727
 
4.9%
a 7727
 
4.9%
f 7727
 
4.9%
t 7727
 
4.9%
e 7727
 
4.9%
Other values (7) 28001
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148726
95.2%
None 7479
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 25715
17.3%
n 22685
15.3%
o 17988
12.1%
d 15454
10.4%
I 7727
 
5.2%
c 7727
 
5.2%
a 7727
 
5.2%
f 7727
 
5.2%
t 7727
 
5.2%
e 7727
 
5.2%
Other values (6) 20522
13.8%
None
ValueCountFrequency (%)
ô 7479
100.0%

id_forma
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2
15783 
4
 
480
5
 
291
3
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16597
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

Length

2023-07-06T12:37:27.292060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:27.468364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 16597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15783
95.1%
4 480
 
2.9%
5 291
 
1.8%
3 43
 
0.3%

de_forma
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Internet (portal)
15783 
Web-mail (email)
 
480
Telefone
 
291
Pessoalmente
 
43

Length

Max length17
Median length17
Mean length16.800325
Min length8

Characters and Unicode

Total characters278835
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternet (portal)
2nd rowInternet (portal)
3rd rowInternet (portal)
4th rowInternet (portal)
5th rowInternet (portal)

Common Values

ValueCountFrequency (%)
Internet (portal) 15783
95.1%
Web-mail (email) 480
 
2.9%
Telefone 291
 
1.8%
Pessoalmente 43
 
0.3%

Length

2023-07-06T12:37:27.626300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:27.799577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
internet 15783
48.0%
portal 15783
48.0%
web-mail 480
 
1.5%
email 480
 
1.5%
telefone 291
 
0.9%
pessoalmente 43
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 47392
17.0%
e 33528
12.0%
n 31900
11.4%
r 31566
11.3%
l 17077
 
6.1%
a 16786
 
6.0%
16263
 
5.8%
( 16263
 
5.8%
) 16263
 
5.8%
o 16117
 
5.8%
Other values (11) 35680
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212969
76.4%
Uppercase Letter 16597
 
6.0%
Space Separator 16263
 
5.8%
Open Punctuation 16263
 
5.8%
Close Punctuation 16263
 
5.8%
Dash Punctuation 480
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 47392
22.3%
e 33528
15.7%
n 31900
15.0%
r 31566
14.8%
l 17077
 
8.0%
a 16786
 
7.9%
o 16117
 
7.6%
p 15783
 
7.4%
m 1003
 
0.5%
i 960
 
0.5%
Other values (3) 857
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
I 15783
95.1%
W 480
 
2.9%
T 291
 
1.8%
P 43
 
0.3%
Space Separator
ValueCountFrequency (%)
16263
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16263
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 229566
82.3%
Common 49269
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 47392
20.6%
e 33528
14.6%
n 31900
13.9%
r 31566
13.8%
l 17077
 
7.4%
a 16786
 
7.3%
o 16117
 
7.0%
I 15783
 
6.9%
p 15783
 
6.9%
m 1003
 
0.4%
Other values (7) 2631
 
1.1%
Common
ValueCountFrequency (%)
16263
33.0%
( 16263
33.0%
) 16263
33.0%
- 480
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 47392
17.0%
e 33528
12.0%
n 31900
11.4%
r 31566
11.3%
l 17077
 
6.1%
a 16786
 
6.0%
16263
 
5.8%
( 16263
 
5.8%
) 16263
 
5.8%
o 16117
 
5.8%
Other values (11) 35680
12.8%

id_priorizacao
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
5.0
15951 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47856
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 15951
96.1%
4.0 1
 
< 0.1%
(Missing) 645
 
3.9%

Length

2023-07-06T12:37:27.960547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:28.117935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 15951
> 99.9%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
5 15951
33.3%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31904
66.7%
Other Punctuation 15952
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15952
50.0%
5 15951
50.0%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
5 15951
33.3%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15952
33.3%
0 15952
33.3%
5 15951
33.3%
4 1
 
< 0.1%

de_priorizacao
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing645
Missing (%)3.9%
Memory size129.8 KiB
Normal
15951 
Média
 
1

Length

Max length6
Median length6
Mean length5.9999373
Min length5

Characters and Unicode

Total characters95711
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 15951
96.1%
Média 1
 
< 0.1%
(Missing) 645
 
3.9%

Length

2023-07-06T12:37:28.252846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:28.414458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 15951
> 99.9%
média 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 15952
16.7%
N 15951
16.7%
o 15951
16.7%
r 15951
16.7%
m 15951
16.7%
l 15951
16.7%
M 1
 
< 0.1%
é 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79759
83.3%
Uppercase Letter 15952
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15952
20.0%
o 15951
20.0%
r 15951
20.0%
m 15951
20.0%
l 15951
20.0%
é 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 15951
> 99.9%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 95711
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15952
16.7%
N 15951
16.7%
o 15951
16.7%
r 15951
16.7%
m 15951
16.7%
l 15951
16.7%
M 1
 
< 0.1%
é 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95710
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15952
16.7%
N 15951
16.7%
o 15951
16.7%
r 15951
16.7%
m 15951
16.7%
l 15951
16.7%
M 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
None
ValueCountFrequency (%)
é 1
100.0%

id_municipio
Real number (ℝ)

HIGH CORRELATION 

Distinct554
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75300.725
Minimum27
Maximum108353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:28.571770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile71706
Q176112
median76570
Q377755
95-th percentile79227
Maximum108353
Range108326
Interquartile range (IQR)1643

Descriptive statistics

Standard deviation11146.382
Coefficient of variation (CV)0.1480249
Kurtosis21.145228
Mean75300.725
Median Absolute Deviation (MAD)702
Skewness-4.248985
Sum1.2497661 × 109
Variance1.2424184 × 108
MonotonicityNot monotonic
2023-07-06T12:37:28.765053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76414 2918
 
17.6%
77127 1015
 
6.1%
78700 736
 
4.4%
75680 604
 
3.6%
77755 516
 
3.1%
76970 468
 
2.8%
76066 435
 
2.6%
15890 384
 
2.3%
76180 326
 
2.0%
77178 298
 
1.8%
Other values (544) 8897
53.6%
ValueCountFrequency (%)
27 1
 
< 0.1%
124 1
 
< 0.1%
809 3
< 0.1%
2054 7
< 0.1%
2542 2
 
< 0.1%
3719 1
 
< 0.1%
4006 1
 
< 0.1%
5339 1
 
< 0.1%
6084 1
 
< 0.1%
8974 3
< 0.1%
ValueCountFrequency (%)
108353 53
0.3%
108352 17
 
0.1%
108350 2
 
< 0.1%
108243 1
 
< 0.1%
107077 3
 
< 0.1%
105678 1
 
< 0.1%
105180 12
 
0.1%
105139 2
 
< 0.1%
105074 6
 
< 0.1%
105040 7
 
< 0.1%
Distinct550
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:29.271311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length24
Mean length10.01446
Min length3

Characters and Unicode

Total characters166210
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)1.1%

Sample

1st rowBRASILIA
2nd rowBRASILIA
3rd rowFLORIANOPOLIS
4th rowFLORIANOPOLIS
5th rowGAROPABA
ValueCountFrequency (%)
florianopolis 2918
 
12.8%
sao 1561
 
6.8%
joinville 1015
 
4.4%
do 959
 
4.2%
jose 782
 
3.4%
sul 726
 
3.2%
blumenau 604
 
2.6%
palhoca 516
 
2.3%
itajai 468
 
2.0%
chapeco 435
 
1.9%
Other values (597) 12848
56.3%
2023-07-06T12:37:30.592598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 23163
13.9%
O 21671
13.0%
I 17245
10.4%
L 13138
 
7.9%
R 11373
 
6.8%
S 9823
 
5.9%
N 8997
 
5.4%
E 8115
 
4.9%
U 6745
 
4.1%
6235
 
3.8%
Other values (18) 39705
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 159974
96.2%
Space Separator 6235
 
3.8%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23163
14.5%
O 21671
13.5%
I 17245
10.8%
L 13138
 
8.2%
R 11373
 
7.1%
S 9823
 
6.1%
N 8997
 
5.6%
E 8115
 
5.1%
U 6745
 
4.2%
P 5907
 
3.7%
Other values (16) 33797
21.1%
Space Separator
ValueCountFrequency (%)
6235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 159974
96.2%
Common 6236
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23163
14.5%
O 21671
13.5%
I 17245
10.8%
L 13138
 
8.2%
R 11373
 
7.1%
S 9823
 
6.1%
N 8997
 
5.6%
E 8115
 
5.1%
U 6745
 
4.2%
P 5907
 
3.7%
Other values (16) 33797
21.1%
Common
ValueCountFrequency (%)
6235
> 99.9%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166176
> 99.9%
None 34
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 23163
13.9%
O 21671
13.0%
I 17245
10.4%
L 13138
 
7.9%
R 11373
 
6.8%
S 9823
 
5.9%
N 8997
 
5.4%
E 8115
 
4.9%
U 6745
 
4.1%
6235
 
3.8%
Other values (16) 39671
23.9%
None
ValueCountFrequency (%)
Á 17
50.0%
à 17
50.0%

sg_uf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
SC
15230 
SP
 
415
DF
 
387
PR
 
189
RS
 
147
Other values (20)
 
229

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters33194
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDF
2nd rowDF
3rd rowSC
4th rowSC
5th rowSC

Common Values

ValueCountFrequency (%)
SC 15230
91.8%
SP 415
 
2.5%
DF 387
 
2.3%
PR 189
 
1.1%
RS 147
 
0.9%
RJ 62
 
0.4%
MG 61
 
0.4%
GO 17
 
0.1%
SE 16
 
0.1%
BA 10
 
0.1%
Other values (15) 63
 
0.4%

Length

2023-07-06T12:37:30.959670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sc 15230
91.8%
sp 415
 
2.5%
df 387
 
2.3%
pr 189
 
1.1%
rs 147
 
0.9%
rj 62
 
0.4%
mg 61
 
0.4%
go 17
 
0.1%
se 16
 
0.1%
ba 10
 
0.1%
Other values (15) 63
 
0.4%

Most occurring characters

ValueCountFrequency (%)
S 15823
47.7%
C 15236
45.9%
P 623
 
1.9%
R 402
 
1.2%
D 387
 
1.2%
F 387
 
1.2%
M 80
 
0.2%
G 78
 
0.2%
J 62
 
0.2%
E 32
 
0.1%
Other values (7) 84
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33194
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 15823
47.7%
C 15236
45.9%
P 623
 
1.9%
R 402
 
1.2%
D 387
 
1.2%
F 387
 
1.2%
M 80
 
0.2%
G 78
 
0.2%
J 62
 
0.2%
E 32
 
0.1%
Other values (7) 84
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 33194
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 15823
47.7%
C 15236
45.9%
P 623
 
1.9%
R 402
 
1.2%
D 387
 
1.2%
F 387
 
1.2%
M 80
 
0.2%
G 78
 
0.2%
J 62
 
0.2%
E 32
 
0.1%
Other values (7) 84
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 15823
47.7%
C 15236
45.9%
P 623
 
1.9%
R 402
 
1.2%
D 387
 
1.2%
F 387
 
1.2%
M 80
 
0.2%
G 78
 
0.2%
J 62
 
0.2%
E 32
 
0.1%
Other values (7) 84
 
0.3%

de_bairro
Text

MISSING 

Distinct2938
Distinct (%)30.5%
Missing6964
Missing (%)42.0%
Memory size129.8 KiB
2023-07-06T12:37:31.261768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length50
Median length46
Mean length10.098412
Min length1

Characters and Unicode

Total characters97278
Distinct characters98
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1773 ?
Unique (%)18.4%

Sample

1st rowagronomica
2nd rowPântano do Sul
3rd rowSanta cecilia
4th rowCampeche
5th rowSanta cecilia
ValueCountFrequency (%)
centro 1698
 
10.9%
do 449
 
2.9%
são 402
 
2.6%
vila 316
 
2.0%
nova 268
 
1.7%
rio 266
 
1.7%
jardim 239
 
1.5%
de 203
 
1.3%
da 193
 
1.2%
santa 185
 
1.2%
Other values (1801) 11398
73.0%
2023-07-06T12:37:31.817432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11110
 
11.4%
o 8904
 
9.2%
r 8347
 
8.6%
e 7150
 
7.4%
i 6321
 
6.5%
6004
 
6.2%
n 4940
 
5.1%
t 4706
 
4.8%
s 3737
 
3.8%
d 2608
 
2.7%
Other values (88) 33451
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73897
76.0%
Uppercase Letter 17037
 
17.5%
Space Separator 6004
 
6.2%
Decimal Number 197
 
0.2%
Other Punctuation 79
 
0.1%
Dash Punctuation 29
 
< 0.1%
Close Punctuation 16
 
< 0.1%
Open Punctuation 14
 
< 0.1%
Connector Punctuation 3
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11110
15.0%
o 8904
12.0%
r 8347
11.3%
e 7150
9.7%
i 6321
8.6%
n 4940
 
6.7%
t 4706
 
6.4%
s 3737
 
5.1%
d 2608
 
3.5%
l 2513
 
3.4%
Other values (28) 13561
18.4%
Uppercase Letter
ValueCountFrequency (%)
C 2501
14.7%
A 1559
 
9.2%
S 1450
 
8.5%
R 1259
 
7.4%
I 1212
 
7.1%
P 994
 
5.8%
E 884
 
5.2%
N 832
 
4.9%
B 801
 
4.7%
O 762
 
4.5%
Other values (26) 4783
28.1%
Decimal Number
ValueCountFrequency (%)
1 35
17.8%
0 31
15.7%
2 28
14.2%
3 24
12.2%
9 21
10.7%
5 16
8.1%
8 15
7.6%
4 11
 
5.6%
7 9
 
4.6%
6 7
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 30
38.0%
, 24
30.4%
/ 17
21.5%
' 5
 
6.3%
: 1
 
1.3%
? 1
 
1.3%
& 1
 
1.3%
Space Separator
ValueCountFrequency (%)
6004
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90934
93.5%
Common 6344
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11110
 
12.2%
o 8904
 
9.8%
r 8347
 
9.2%
e 7150
 
7.9%
i 6321
 
7.0%
n 4940
 
5.4%
t 4706
 
5.2%
s 3737
 
4.1%
d 2608
 
2.9%
l 2513
 
2.8%
Other values (64) 30598
33.6%
Common
ValueCountFrequency (%)
6004
94.6%
1 35
 
0.6%
0 31
 
0.5%
. 30
 
0.5%
- 29
 
0.5%
2 28
 
0.4%
, 24
 
0.4%
3 24
 
0.4%
9 21
 
0.3%
/ 17
 
0.3%
Other values (14) 101
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95272
97.9%
None 2006
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11110
 
11.7%
o 8904
 
9.3%
r 8347
 
8.8%
e 7150
 
7.5%
i 6321
 
6.6%
6004
 
6.3%
n 4940
 
5.2%
t 4706
 
4.9%
s 3737
 
3.9%
d 2608
 
2.7%
Other values (65) 31445
33.0%
None
ValueCountFrequency (%)
ã 764
38.1%
ç 245
 
12.2%
á 225
 
11.2%
ó 182
 
9.1%
é 122
 
6.1%
í 79
 
3.9%
ô 71
 
3.5%
Á 57
 
2.8%
ê 51
 
2.5%
â 48
 
2.4%
Other values (13) 162
 
8.1%

de_cep
Text

MISSING 

Distinct5422
Distinct (%)54.5%
Missing6653
Missing (%)40.1%
Memory size129.8 KiB
2023-07-06T12:37:32.175639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters119328
Distinct characters55
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3957 ?
Unique (%)39.8%

Sample

1st row88067001
2nd row89540000
3rd row88063-082
4th row89540000
5th row89270000
ValueCountFrequency (%)
88220000 85
 
0.9%
89240000 56
 
0.6%
88115730 53
 
0.5%
88780000 50
 
0.5%
88.010-102 50
 
0.5%
88495000 46
 
0.5%
88220-000 44
 
0.4%
89520000 41
 
0.4%
89249000 39
 
0.4%
89520-000 38
 
0.4%
Other values (5420) 9475
95.0%
2023-07-06T12:37:32.678659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36499
30.6%
0 24331
20.4%
8 18051
15.1%
1 5954
 
5.0%
9 5787
 
4.8%
2 5363
 
4.5%
3 5035
 
4.2%
5 4925
 
4.1%
4 3546
 
3.0%
6 3239
 
2.7%
Other values (45) 6598
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79310
66.5%
Space Separator 36500
30.6%
Dash Punctuation 3063
 
2.6%
Other Punctuation 331
 
0.3%
Lowercase Letter 84
 
0.1%
Uppercase Letter 24
 
< 0.1%
Connector Punctuation 13
 
< 0.1%
Control 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 12
14.3%
o 10
11.9%
g 8
9.5%
e 7
8.3%
a 6
 
7.1%
m 6
 
7.1%
l 5
 
6.0%
n 5
 
6.0%
s 4
 
4.8%
t 4
 
4.8%
Other values (11) 17
20.2%
Uppercase Letter
ValueCountFrequency (%)
N 4
16.7%
C 4
16.7%
A 3
12.5%
I 2
8.3%
J 2
8.3%
S 1
 
4.2%
T 1
 
4.2%
E 1
 
4.2%
R 1
 
4.2%
O 1
 
4.2%
Other values (4) 4
16.7%
Decimal Number
ValueCountFrequency (%)
0 24331
30.7%
8 18051
22.8%
1 5954
 
7.5%
9 5787
 
7.3%
2 5363
 
6.8%
3 5035
 
6.3%
5 4925
 
6.2%
4 3546
 
4.5%
6 3239
 
4.1%
7 3079
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 323
97.6%
, 5
 
1.5%
' 2
 
0.6%
/ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
36499
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
2
66.7%
” 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 3063
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119220
99.9%
Latin 108
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 12
 
11.1%
o 10
 
9.3%
g 8
 
7.4%
e 7
 
6.5%
a 6
 
5.6%
m 6
 
5.6%
l 5
 
4.6%
n 5
 
4.6%
N 4
 
3.7%
C 4
 
3.7%
Other values (25) 41
38.0%
Common
ValueCountFrequency (%)
36499
30.6%
0 24331
20.4%
8 18051
15.1%
1 5954
 
5.0%
9 5787
 
4.9%
2 5363
 
4.5%
3 5035
 
4.2%
5 4925
 
4.1%
4 3546
 
3.0%
6 3239
 
2.7%
Other values (10) 6490
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119321
> 99.9%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36499
30.6%
0 24331
20.4%
8 18051
15.1%
1 5954
 
5.0%
9 5787
 
4.8%
2 5363
 
4.5%
3 5035
 
4.2%
5 4925
 
4.1%
4 3546
 
3.0%
6 3239
 
2.7%
Other values (40) 6591
 
5.5%
None
ValueCountFrequency (%)
ã 3
42.9%
  1
 
14.3%
ó 1
 
14.3%
” 1
 
14.3%
ç 1
 
14.3%

id_municipio_fato_ocorrido
Real number (ℝ)

HIGH CORRELATION 

Distinct289
Distinct (%)1.7%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean77185.705
Minimum75159
Maximum108353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2023-07-06T12:37:32.888697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum75159
5-th percentile75400
Q176180
median76511
Q377607
95-th percentile79022
Maximum108353
Range33194
Interquartile range (IQR)1427

Descriptive statistics

Standard deviation3020.5137
Coefficient of variation (CV)0.039133071
Kurtosis72.517259
Mean77185.705
Median Absolute Deviation (MAD)616
Skewness7.9347816
Sum1.2795846 × 109
Variance9123502.9
MonotonicityNot monotonic
2023-07-06T12:37:33.092038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76414 3283
 
19.8%
77127 1033
 
6.2%
78700 711
 
4.3%
75680 637
 
3.8%
76970 512
 
3.1%
77755 505
 
3.0%
76066 450
 
2.7%
76180 359
 
2.2%
75493 318
 
1.9%
77178 301
 
1.8%
Other values (279) 8469
51.0%
ValueCountFrequency (%)
75159 31
 
0.2%
75167 38
 
0.2%
75175 48
 
0.3%
75183 158
1.0%
75191 33
 
0.2%
75213 39
 
0.2%
75221 27
 
0.2%
75230 52
 
0.3%
75272 30
 
0.2%
75302 13
 
0.1%
ValueCountFrequency (%)
108353 54
0.3%
108352 21
 
0.1%
107077 6
 
< 0.1%
105180 12
 
0.1%
105139 2
 
< 0.1%
105074 11
 
0.1%
105040 7
 
< 0.1%
97314 21
 
0.1%
96261 15
 
0.1%
96253 3
 
< 0.1%
Distinct289
Distinct (%)1.7%
Missing19
Missing (%)0.1%
Memory size129.8 KiB
2023-07-06T12:37:33.494716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length23
Mean length10.159247
Min length3

Characters and Unicode

Total characters168420
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowAGRONOMICA
2nd rowAGROLANDIA
3rd rowFLORIANOPOLIS
4th rowFLORIANOPOLIS
5th rowIMBITUBA
ValueCountFrequency (%)
florianopolis 3283
 
14.5%
sao 1341
 
5.9%
joinville 1033
 
4.5%
do 980
 
4.3%
jose 751
 
3.3%
sul 748
 
3.3%
blumenau 637
 
2.8%
itajai 512
 
2.3%
palhoca 505
 
2.2%
chapeco 489
 
2.2%
Other values (340) 12434
54.7%
2023-07-06T12:37:34.092853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 23143
13.7%
O 22588
13.4%
I 17276
10.3%
L 13427
 
8.0%
R 11546
 
6.9%
S 9622
 
5.7%
N 9549
 
5.7%
E 8057
 
4.8%
U 6667
 
4.0%
6135
 
3.6%
Other values (17) 40410
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162285
96.4%
Space Separator 6135
 
3.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23143
14.3%
O 22588
13.9%
I 17276
10.6%
L 13427
 
8.3%
R 11546
 
7.1%
S 9622
 
5.9%
N 9549
 
5.9%
E 8057
 
5.0%
U 6667
 
4.1%
P 5998
 
3.7%
Other values (16) 34412
21.2%
Space Separator
ValueCountFrequency (%)
6135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162285
96.4%
Common 6135
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23143
14.3%
O 22588
13.9%
I 17276
10.6%
L 13427
 
8.3%
R 11546
 
7.1%
S 9622
 
5.9%
N 9549
 
5.9%
E 8057
 
5.0%
U 6667
 
4.1%
P 5998
 
3.7%
Other values (16) 34412
21.2%
Common
ValueCountFrequency (%)
6135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168378
> 99.9%
None 42
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 23143
13.7%
O 22588
13.4%
I 17276
10.3%
L 13427
 
8.0%
R 11546
 
6.9%
S 9622
 
5.7%
N 9549
 
5.7%
E 8057
 
4.8%
U 6667
 
4.0%
6135
 
3.6%
Other values (15) 40368
24.0%
None
ValueCountFrequency (%)
Á 21
50.0%
à 21
50.0%

de_outro_local
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16597
Missing (%)100.0%
Memory size129.8 KiB

de_sexo_solicitante
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing382
Missing (%)2.3%
Memory size129.8 KiB
N
7794 
M
4218 
F
4203 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowM
4th rowN
5th rowM

Common Values

ValueCountFrequency (%)
N 7794
47.0%
M 4218
25.4%
F 4203
25.3%
(Missing) 382
 
2.3%

Length

2023-07-06T12:37:34.288654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:34.452973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
n 7794
48.1%
m 4218
26.0%
f 4203
25.9%

Most occurring characters

ValueCountFrequency (%)
N 7794
48.1%
M 4218
26.0%
F 4203
25.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16215
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 7794
48.1%
M 4218
26.0%
F 4203
25.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 16215
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 7794
48.1%
M 4218
26.0%
F 4203
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 7794
48.1%
M 4218
26.0%
F 4203
25.9%

nu_idade_solicitante
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16597
Missing (%)100.0%
Memory size129.8 KiB

de_forma_tratamento
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing7896
Missing (%)47.6%
Memory size129.8 KiB
Sr
3798 
Você
2589 
Senhora
1869 
Senhorita
445 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters130515
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSr
2nd rowVocê
3rd rowSenhora
4th rowVocê
5th rowVocê

Common Values

ValueCountFrequency (%)
Sr 3798
22.9%
Você 2589
 
15.6%
Senhora 1869
 
11.3%
Senhorita 445
 
2.7%
(Missing) 7896
47.6%

Length

2023-07-06T12:37:34.592219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-06T12:37:34.775954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sr 3798
43.7%
você 2589
29.8%
senhora 1869
21.5%
senhorita 445
 
5.1%

Most occurring characters

ValueCountFrequency (%)
95475
73.2%
S 6112
 
4.7%
r 6112
 
4.7%
o 4903
 
3.8%
V 2589
 
2.0%
c 2589
 
2.0%
ê 2589
 
2.0%
e 2314
 
1.8%
n 2314
 
1.8%
h 2314
 
1.8%
Other values (3) 3204
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 95475
73.2%
Lowercase Letter 26339
 
20.2%
Uppercase Letter 8701
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 6112
23.2%
o 4903
18.6%
c 2589
9.8%
ê 2589
9.8%
e 2314
 
8.8%
n 2314
 
8.8%
h 2314
 
8.8%
a 2314
 
8.8%
i 445
 
1.7%
t 445
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
S 6112
70.2%
V 2589
29.8%
Space Separator
ValueCountFrequency (%)
95475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95475
73.2%
Latin 35040
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 6112
17.4%
r 6112
17.4%
o 4903
14.0%
V 2589
7.4%
c 2589
7.4%
ê 2589
7.4%
e 2314
 
6.6%
n 2314
 
6.6%
h 2314
 
6.6%
a 2314
 
6.6%
Other values (2) 890
 
2.5%
Common
ValueCountFrequency (%)
95475
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127926
98.0%
None 2589
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95475
74.6%
S 6112
 
4.8%
r 6112
 
4.8%
o 4903
 
3.8%
V 2589
 
2.0%
c 2589
 
2.0%
e 2314
 
1.8%
n 2314
 
1.8%
h 2314
 
1.8%
a 2314
 
1.8%
Other values (2) 890
 
0.7%
None
ValueCountFrequency (%)
ê 2589
100.0%

Interactions

2023-07-06T12:37:12.531686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:04.399881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:06.427065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.645562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.014331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.230296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.366842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:12.728796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:04.634099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:06.612648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.822735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.198540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.403774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.533803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:12.900958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:04.920232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:06.789475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.996787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.367330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.578087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.716878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:13.098310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:05.229748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:06.962556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:08.336752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.558739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.747552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.884285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:13.286129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:05.527215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.138745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:08.520258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.727381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.906980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:12.048061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:13.453510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:05.815498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.299745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:08.681299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:09.886599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.049641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:12.205000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:13.850130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:06.124311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:07.459789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:08.838999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:10.055860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:11.207422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-06T12:37:12.357832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-06T12:37:34.946038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
nu_atendimentoid_atendimentoid_naturezaid_assuntoid_areaid_municipioid_municipio_fato_ocorridosg_status_atendimentode_status_atendimentode_naturezaid_programade_programade_areaid_tp_identificacaode_tp_identificacaoid_formade_formaid_priorizacaode_priorizacaosg_ufde_sexo_solicitantede_forma_tratamento
nu_atendimento1.0001.000-0.0840.076-0.0530.0060.0070.2920.2920.0890.0720.0720.0790.0690.0690.0510.0510.0000.0000.0360.0570.040
id_atendimento1.0001.000-0.0840.076-0.0530.0060.0070.2930.2930.0890.0720.0720.0790.0700.0700.0500.0500.0000.0000.0360.0560.040
id_natureza-0.084-0.0841.0000.0450.333-0.008-0.0070.0150.0151.0000.1770.1770.4220.1240.1240.3330.3330.0000.0000.3150.1360.055
id_assunto0.0760.0760.0451.000-0.035-0.011-0.0310.1400.1400.1370.1180.1180.3370.1680.1680.1290.1290.0000.0000.1030.1450.056
id_area-0.053-0.0530.333-0.0351.000-0.042-0.0370.1000.1000.1970.2580.2580.9990.2170.2170.1670.1670.0120.0120.1730.2130.074
id_municipio0.0060.006-0.008-0.011-0.0421.0000.6910.0210.0210.2470.0560.0560.3180.1190.1190.4280.4280.0000.0000.7630.1230.028
id_municipio_fato_ocorrido0.0070.007-0.007-0.031-0.0370.6911.0000.0000.0000.0190.0140.0140.1030.0220.0220.0000.0000.0000.0000.0490.0150.024
sg_status_atendimento0.2920.2930.0150.1400.1000.0210.0001.0001.0000.0380.0420.0420.2300.0380.0380.0140.0140.0000.0000.0000.0360.007
de_status_atendimento0.2920.2930.0150.1400.1000.0210.0001.0001.0000.0380.0420.0420.2300.0380.0380.0140.0140.0000.0000.0000.0360.007
de_natureza0.0890.0891.0000.1370.1970.2470.0190.0380.0381.0000.2330.2330.3690.1900.1900.3440.3440.0000.0000.2660.2130.060
id_programa0.0720.0720.1770.1180.2580.0560.0140.0420.0420.2331.0001.0000.3150.1340.1340.0340.0340.0000.0000.0650.1150.058
de_programa0.0720.0720.1770.1180.2580.0560.0140.0420.0420.2331.0001.0000.3150.1340.1340.0340.0340.0000.0000.0650.1150.058
de_area0.0790.0790.4220.3370.9990.3180.1030.2300.2300.3690.3150.3151.0000.2990.2990.5150.5150.0000.0000.2090.2980.109
id_tp_identificacao0.0690.0700.1240.1680.2170.1190.0220.0380.0380.1900.1340.1340.2991.0001.0000.1170.1170.0000.0000.1330.6600.142
de_tp_identificacao0.0690.0700.1240.1680.2170.1190.0220.0380.0380.1900.1340.1340.2991.0001.0000.1170.1170.0000.0000.1330.6600.142
id_forma0.0510.0500.3330.1290.1670.4280.0000.0140.0140.3440.0340.0340.5150.1170.1171.0001.0000.0000.0000.4400.0890.105
de_forma0.0510.0500.3330.1290.1670.4280.0000.0140.0140.3440.0340.0340.5150.1170.1171.0001.0000.0000.0000.4400.0890.105
id_priorizacao0.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.5000.0000.0080.000
de_priorizacao0.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5001.0000.0000.0080.000
sg_uf0.0360.0360.3150.1030.1730.7630.0490.0000.0000.2660.0650.0650.2090.1330.1330.4400.4400.0000.0001.0000.1440.046
de_sexo_solicitante0.0570.0560.1360.1450.2130.1230.0150.0360.0360.2130.1150.1150.2980.6600.6600.0890.0890.0080.0080.1441.0000.516
de_forma_tratamento0.0400.0400.0550.0560.0740.0280.0240.0070.0070.0600.0580.0580.1090.1420.1420.1050.1050.0000.0000.0460.5161.000

Missing values

2023-07-06T12:37:14.281273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-06T12:37:15.136122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-06T12:37:15.791670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nu_atendimentoano_atendimentoid_atendimentoch_atendimentosg_status_atendimentode_status_atendimentofg_ativodt_criacaodt_entradadt_alteracaodt_prazoid_orgaonm_orgaosg_orgaonome_familiastatusid_tp_orgaoid_naturezade_naturezaid_assuntode_assuntoid_programade_programaid_areade_areaid_tp_identificacaode_tp_identificacaoid_formade_formaid_priorizacaode_priorizacaoid_municipionm_municipiosg_ufde_bairrode_cepid_municipio_fato_ocorridomn_municipio_fatode_outro_localde_sexo_solicitantenu_idade_solicitantede_forma_tratamento
0120234664992023000001CConcluidoTrue2023-01-01 03:29:01.614892023-01-02 09:09:38.02023-01-01 03:29:01.614892023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93Solicitação1449.0Não Foi Possível Compreender4.0Ouvidoria externa203.0Não Identificável1Anônimo2Internet (portal)5.0Normal15890BRASILIADFNoneNone75183.0AGRONOMICANaNNNaNNone
1220234665002023000002CConcluidoTrue2023-01-01 05:18:44.506682023-01-02 09:12:26.02023-01-01 05:18:44.506682023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93Solicitação1449.0Não Foi Possível Compreender4.0Ouvidoria externa203.0Não Identificável1Anônimo2Internet (portal)5.0Normal15890BRASILIADFNoneNone75175.0AGROLANDIANaNNNaNNone
2420234665022023000004CConcluidoTrue2023-01-01 13:38:35.0750222023-01-02 09:13:46.02023-01-01 13:38:35.0750222023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação1611.0Manifestação Incompleta (Falta Dados)5.0Ouvidoria interna203.0Não Identificável2Identificado2Internet (portal)5.0Normal76414FLORIANOPOLISSCagronomicaNone76414.0FLORIANOPOLISNaNMNaNSr
3520234665032023000005CConcluidoTrue2023-01-01 14:24:08.3168252023-01-02 09:15:11.02023-01-01 14:24:08.3168252023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93Solicitação242.0Fiscalização ambiental4.0Ouvidoria externa170.0FATMA1Anônimo2Internet (portal)5.0Normal76414FLORIANOPOLISSCPântano do Sul8806700176414.0FLORIANOPOLISNaNNNaNNone
4620234665042023000006CConcluidoTrue2023-01-01 14:50:22.9947922023-01-02 09:16:57.02023-01-01 14:50:22.9947922023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação1611.0Manifestação Incompleta (Falta Dados)4.0Ouvidoria externa155.0Segurança Pública2Identificado2Internet (portal)5.0Normal76490GAROPABASCNoneNone76759.0IMBITUBANaNMNaNVocê
5720234665052023000007CConcluidoTrue2023-01-01 17:35:14.0210652023-01-02 09:19:33.02023-01-01 17:35:14.0210652023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação117.0Comportamento do servidor4.0Ouvidoria externa4.0Educação1Anônimo2Internet (portal)5.0Normal76970ITAJAISCNoneNone76970.0ITAJAINaNNNaNNone
6820234665062023000008CConcluidoTrue2023-01-01 18:31:10.8733012023-01-02 09:22:01.02023-01-01 18:31:10.8733012023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação1611.0Manifestação Incompleta (Falta Dados)4.0Ouvidoria externa203.0Não Identificável1Anônimo2Internet (portal)5.0Normal76201CURITIBANOSSCSanta cecilia8954000078450.0SANTA CECILIANaNNNaNNone
7920234665072023000009CConcluidoTrue2023-01-01 18:46:08.837362023-01-02 09:28:02.02023-01-01 18:46:08.837362023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação555.0Vazamento de Água4.0Ouvidoria externa30.0Água e saneamento/CASAN2Identificado2Internet (portal)5.0Normal76414FLORIANOPOLISSCCampeche88063-08276414.0FLORIANOPOLISNaNFNaNSenhora
81020234665082023000010CConcluidoTrue2023-01-01 19:23:05.7373032023-01-02 09:29:48.02023-01-01 19:23:05.7373032023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação1611.0Manifestação Incompleta (Falta Dados)4.0Ouvidoria externa203.0Não Identificável1Anônimo2Internet (portal)5.0Normal76201CURITIBANOSSCSanta cecilia8954000078450.0SANTA CECILIANaNNNaNNone
91120234665092023000011CConcluidoTrue2023-01-01 19:46:50.2713922023-01-02 09:31:28.02023-01-01 19:46:50.2713922023-01-312Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92Reclamação393.0Multas de trânsito4.0Ouvidoria externa126.0DETRAN2Identificado2Internet (portal)5.0Normal76600GUARAMIRIMSCIlha da figueira8927000078875.0SCHROEDERNaNMNaNVocê
nu_atendimentoano_atendimentoid_atendimentoch_atendimentosg_status_atendimentode_status_atendimentofg_ativodt_criacaodt_entradadt_alteracaodt_prazoid_orgaonm_orgaosg_orgaonome_familiastatusid_tp_orgaoid_naturezade_naturezaid_assuntode_assuntoid_programade_programaid_areade_areaid_tp_identificacaode_tp_identificacaoid_formade_formaid_priorizacaode_priorizacaoid_municipionm_municipiosg_ufde_bairrode_cepid_municipio_fato_ocorridomn_municipio_fatode_outro_localde_sexo_solicitantenu_idade_solicitantede_forma_tratamento
165871887820234855872023018261PPendenteTrue2023-07-05 21:54:40.721457None2023-07-05 21:54:40.7214572023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone1Anônimo2Internet (portal)NaNNone75817CACADORSCNoneNone75817.0CACADORNaNNoneNaNNone
165881887920234855882023018262PPendenteTrue2023-07-05 22:05:29.155974None2023-07-05 22:05:29.1559742023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA92ReclamaçãoNaNNoneNaNNoneNaNNone2Identificado2Internet (portal)NaNNone76970ITAJAISCCentro8830124076970.0ITAJAINaNMNaNSr
165891888020234855892023018263PPendenteTrue2023-07-05 22:09:39.710053None2023-07-05 22:09:39.7100532023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone1Anônimo2Internet (portal)NaNNone77798PALMITOSSCNoneNone77798.0PALMITOSNaNNoneNaNNone
165901888120234855902023018264PPendenteTrue2023-07-05 22:29:47.667348None2023-07-05 22:29:47.6673482023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone3Sigiloso2Internet (portal)NaNNone77771BRUNOPOLISSCNoneNone77771.0BRUNOPOLISNaNMNaNSr
165911888220234855912023018265PPendenteTrue2023-07-05 22:32:22.648808None2023-07-05 22:32:22.6488082023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93SolicitaçãoNaNNoneNaNNoneNaNNone3Sigiloso2Internet (portal)NaNNone78093POUSO REDONDOSCNoneNone78093.0POUSO REDONDONaNFNaNSenhorita
165921888320234855922023018266PPendenteTrue2023-07-05 22:33:47.286463None2023-07-05 22:33:47.2864632023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone1Anônimo2Internet (portal)NaNNone77127JOINVILLESCJoão costa8920947277127.0JOINVILLENaNNNaNSr
165931888420234855932023018267PPendenteTrue2023-07-05 22:50:19.63995None2023-07-05 22:50:19.639952023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone3Sigiloso2Internet (portal)NaNNone77089JAGUARUNASCNoneNone79090.0TUBARAONaNMNaNVocê
165941888520234855942023018268PPendenteTrue2023-07-05 23:03:39.961797None2023-07-05 23:03:39.9617972023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA91DenúnciaNaNNoneNaNNoneNaNNone1Anônimo2Internet (portal)NaNNone75680BLUMENAUSCVorstadt8901525275680.0BLUMENAUNaNNoneNaNNone
165951888620234855952023018269PPendenteTrue2023-07-05 23:26:01.697794None2023-07-05 23:26:01.6977942023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93SolicitaçãoNaNNoneNaNNoneNaNNone3Sigiloso2Internet (portal)NaNNone75680BLUMENAUSCFortaleza89057-30075680.0BLUMENAUNaNFNaNSr
165961888720234855962023018270PPendenteTrue2023-07-05 23:29:08.4676None2023-07-05 23:29:08.46762023-08-042Ouvidoria Geral do EstadoOGESC::Ouvidoria Geral do Estado - OGEA93SolicitaçãoNaNNoneNaNNoneNaNNone2Identificado2Internet (portal)NaNNone77755PALHOCASCPonte do Imaruim8813051076414.0FLORIANOPOLISNaNMNaNSr